Is this policy legally binding?

This template creates an internal governance document, not a legal contract. However, the commitments you make (especially in transparency and data usage) should align with your public privacy policy and terms of service. Have your legal team review the final policy to ensure consistency with external commitments and applicable AI regulations like the [EU AI Act](/glossary/prioritization).

How is this different from a company-wide AI ethics policy?

A company-wide ethics policy sets principles. This template sets operational requirements for a specific product. Think of ethics principles as the constitution and this policy as the law. The policy translates abstract principles like "we are committed to fairness" into measurable thresholds, specific procedures, and named owners.

What if our AI model is a third-party API?

The policy still applies. You are responsible for how AI affects your users, regardless of who built the model. The acceptable use, transparency, bias monitoring, and incident response sections apply equally to third-party models. Add a section on vendor management that documents the provider's safety commitments and your contractual rights to audit.

How do we enforce this policy in practice?

Enforcement happens at three levels. Pre-launch: the policy checklist is part of the launch review. Runtime: monitoring and alerting detect violations automatically. Post-incident: the review process identifies gaps and updates the policy. Assign a named owner for each section who is accountable for compliance.

Should we publish this policy externally?

Consider publishing a user-facing version that covers transparency, data usage, and user rights. Keep the internal operational details (incident response, bias thresholds, model specifics) internal. A public-facing responsible AI page builds user trust and demonstrates accountability.

AI Responsible Use Policy Template

What This Template Is For

Shipping AI without a responsible use policy is like shipping a product without a privacy policy: it works until it does not, and then the consequences are severe. AI products can generate harmful content, amplify biases, leak private data, or make decisions that affect people's lives. A responsible use policy defines the boundaries your AI must operate within and the processes for handling violations.

This template is designed for product teams, not legal departments. It translates abstract AI ethics principles into concrete product requirements, engineering constraints, and operational procedures. Each section includes checkboxes your team can work through and specific policy language you can adapt.

The AI PM Handbook dedicates a full chapter to responsible AI product development. For a quick risk assessment, use the AI Ethics Scanner. The Responsible AI Framework provides the strategic foundation this policy builds on.

How to Use This Template

Start with the Scope section to define exactly which AI features and models this policy covers. A policy that covers everything covers nothing. Be specific.

Work through Acceptable Use with your product and legal team. Define what your AI is allowed to do, what it must refuse, and the gray areas that require human judgment.

Define transparency requirements with your design team. Users have a right to know when they are interacting with AI and how their data is used.

Document bias mitigation with your ML team. Move beyond "we care about fairness" to specific measurement, thresholds, and remediation procedures.

Build the incident response section with your on-call and trust & safety team. When something goes wrong (and it will), your team needs a playbook, not a committee meeting.

Review quarterly and update after every significant AI incident, model change, or regulatory development.

The Template

Policy Scope

☐ List every AI feature and model covered by this policy
☐ Define the user populations affected (customers, internal users, partners)
☐ Identify the risk tier for each AI feature (critical, high, medium, low)
☐ Name the policy owner and review cadence

## Policy Scope

**Effective Date**: [YYYY-MM-DD]
**Policy Owner**: [Name and title]
**Review Cadence**: [Quarterly / Semi-annually]
**Last Reviewed**: [YYYY-MM-DD]

### Covered AI Features
| Feature | Model | Risk Tier | Users Affected | Owner |
|---------|-------|-----------|---------------|-------|
| [Feature 1] | [Model name/version] | [Critical/High/Med/Low] | [Customer-facing / Internal] | [PM name] |
| [Feature 2] | [Model name/version] | [Critical/High/Med/Low] | [Customer-facing / Internal] | [PM name] |

### Risk Tier Definitions
- **Critical**: AI makes or influences decisions with significant impact on people's health, finances, employment, or legal standing
- **High**: AI generates content shown directly to users without human review
- **Medium**: AI assists human decision-making with human always in the loop
- **Low**: AI operates on internal data for internal users only

Acceptable Use

☐ Define permitted uses of the AI feature
☐ Define prohibited uses (content the AI must never generate)
☐ Define restricted uses (allowed with additional safeguards)
☐ Specify refusal behavior (how the AI declines prohibited requests)
☐ Document the boundary between AI automation and human judgment

## Acceptable Use

### Permitted Uses
The AI may:
- [Specific permitted use 1]
- [Specific permitted use 2]
- [Specific permitted use 3]

### Prohibited Uses
The AI must never:
- [ ] Generate content that could cause physical harm
- [ ] Provide medical, legal, or financial advice as authoritative guidance
- [ ] Generate content that discriminates based on protected characteristics
- [ ] Create deceptive content designed to mislead users
- [ ] Access, store, or transmit PII beyond what is necessary for the task
- [ ] Make autonomous decisions in Critical risk tier features without human review
- [ ] [Product-specific prohibition 1]
- [ ] [Product-specific prohibition 2]

### Restricted Uses (Allowed with Safeguards)
- [Restricted use 1]: Requires [safeguard, e.g., human review before delivery]
- [Restricted use 2]: Requires [safeguard, e.g., confidence threshold > X%]

Transparency Requirements

☐ Define how users are informed they are interacting with AI
☐ Define how AI-generated content is labeled or distinguished
☐ Document data usage disclosure (what data is used, how, and why)
☐ Define user control mechanisms (opt-out, feedback, correction)
☐ Specify documentation requirements for AI decision-making logic

## Transparency

### User Disclosure
- All AI-generated content must be visually marked with [badge/label/icon]
- Users must be informed [before/during/after] interacting with an AI feature
- Disclosure language: "[Your approved disclosure text]"

### Data Usage
- Users are informed about data usage via [privacy policy / in-app notice / consent dialog]
- Users can [opt out of / delete] their data used for model improvement
- Data usage for training requires [explicit consent / legitimate interest basis]

### User Controls
- [ ] Users can provide feedback on AI outputs (thumbs up/down, corrections)
- [ ] Users can opt out of AI features and use manual alternatives
- [ ] Users can request an explanation of AI-assisted decisions
- [ ] Users can appeal AI-assisted decisions to a human reviewer

Bias Mitigation

☐ Define protected characteristics to test for bias
☐ Set maximum acceptable disparity thresholds
☐ Define measurement methodology and evaluation cadence
☐ Document remediation process when bias is detected
☐ Assign ownership for ongoing bias monitoring

## Bias Mitigation

### Protected Characteristics
Test for disparate treatment or impact across:
- [ ] Gender and gender identity
- [ ] Race and ethnicity
- [ ] Age
- [ ] Disability status
- [ ] National origin and language
- [ ] [Product-specific characteristic]

### Measurement
| Metric | Threshold | Measurement Method | Cadence |
|--------|----------|-------------------|---------|
| Output quality parity | < [X]% variance across groups | [A/B testing with demographic segments] | [Monthly] |
| Error rate parity | < [X]% variance across groups | [Automated evaluation on segmented test set] | [Monthly] |
| Refusal rate parity | < [X]% variance across groups | [Log analysis by user segment] | [Weekly] |

### Remediation Process
1. Bias detected → Alert sent to [owner]
2. Within [24 hours]: Assess severity and user impact
3. Within [1 week]: Implement mitigation (prompt adjustment, training data rebalancing, model constraint)
4. Within [2 weeks]: Verify fix with updated evaluation
5. Document findings and remediation in [location]

Incident Escalation

☐ Define what constitutes an AI safety incident
☐ Build an escalation matrix with response times
☐ Assign incident commanders for each severity level
☐ Define communication protocols (internal and external)
☐ Document post-incident review process

## Incident Response

### Incident Classification
| Severity | Definition | Response Time | Escalation |
|----------|-----------|---------------|------------|
| P0 - Critical | AI causes or could cause physical, financial, or legal harm to users | < 1 hour | VP Engineering + Legal + Comms |
| P1 - High | AI generates harmful, biased, or severely inaccurate content at scale | < 4 hours | Engineering Manager + PM Lead |
| P2 - Medium | AI quality degrades significantly or AI leaks internal data | < 24 hours | PM + ML Engineer on-call |
| P3 - Low | Individual report of inappropriate AI output | < 1 week | ML Engineer on-call |

### Response Actions
- **P0**: Disable AI feature immediately. Notify affected users. External communication.
- **P1**: Increase monitoring. Prepare to disable if not resolved in [X] hours.
- **P2**: Investigate root cause. Deploy fix within [X] days.
- **P3**: Log finding. Address in next sprint.

Filled Example

Product: AI-powered hiring assistant that screens resumes and suggests interview questions.

Risk Tier: Critical (AI influences employment decisions).

Acceptable Use: The AI may score resumes against job requirements and suggest interview questions. The AI must never auto-reject candidates, make hiring decisions, or use demographic information (name, photo, age, location) in scoring. All AI suggestions require recruiter review and approval before action.

Transparency: Every AI-scored resume displays a "Scored by AI" badge and a link to "How our AI works." Candidates can request a human-only review. The privacy policy discloses that resume data is used for scoring but not for model training without explicit consent.

Bias Measurement: Monthly evaluation across gender and ethnicity using a synthetic test set of 1,000 resumes with controlled demographic signals. Maximum acceptable pass-rate variance: 5% across any two groups. Current variance: 3.2% (within threshold).

Incident Example: In March 2026, a user reported that the AI consistently scored candidates from non-English-speaking countries lower. Investigation revealed that the model penalized non-standard resume formats common in those countries. Fix: retrained with format-diverse examples and added format normalization preprocessing. Variance dropped from 8.1% to 2.7%.

AI Responsible Use Policy Template